Skorch:如何绘制训练和验证准确性

import numpy as np
import matplotlib.pyplot as plt
from skorch import NeuralNetClassifier
from skorch.callbacks import EpochScoring

import torch
import torch.nn as nn

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        # 定义模型结构

    def forward(self, x):
        # 定义前向传播逻辑
        return x

model = NeuralNetClassifier(
    MyModel,
    optimizer=torch.optim.Adam,
    criterion=nn.CrossEntropyLoss,
    max_epochs=10,
    lr=0.001,
)

accuracy = EpochScoring(scoring='accuracy', lower_is_better=False)

model.set_params(callbacks=[accuracy])

X_train, y_train = ...
X_val, y_val = ...

model.fit(X_train, y_train)

train_acc = model.history[:, 'train_accuracy']
valid_acc = model.history[:, 'valid_accuracy']

plt.plot(train_acc, label='Train Accuracy')
plt.plot(valid_acc, label='Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()